Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems - Schahin Tofangchi
-20% with code BOOKS
Shipping in 15-21 days
30-day return policy
The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the ... Full description
You May Also Like
TOP
Gödel, Escher, Bach: An Eternal Golden Braid
-20% with code BOOKS
In stock at our supplier
TOP
Hackers. 25th Anniversary Edition: Heroes of the Computer Revolution
-20% with code BOOKS
In stock at our supplier
TOP
The Web Application Hacker's Handbook: Discovering and Exploiting Security Flaws
-20% with code BOOKS
In stock at our supplier
TOP
The Pragmatic Programmer: journey to mastery, 20th Anniversary Edition, 2/e: your journey to mastery, 20th Anniversary Edition
-20% with code BOOKS
In stock at our supplier
TOP
Mathematics for Machine Learning
Cheng Soon Ong, A. Aldo Faisal, Marc Peter Deisenroth
-20% with code BOOKS
In stock at our supplier
TOP
The Art of Game Design: A Book of Lenses
-20% with code BOOKS
In stock at our supplier
TOP
How Linux Works: What Every Superuser Should Know
-20% with code BOOKS
In stock at our supplier
TOP
Artificial Intelligence: A Modern Approach, Global Edition
-20% with code BOOKS
In stock at our supplier
TOP
Linux Basics for Hackers, 2nd Edition: Getting Started with Networking, Scripting, and Security in Kali
-20% with code BOOKS
In stock at our supplier
TOP
AI Engineering: Building Applications with Foundation Models
-20% with code BOOKS
In stock at our supplier
TOP
Fundamentals of Software Architecture: A Modern Engineering Approach
-20% with code BOOKS
In stock at our supplier
TOP
How To Think About AI: A Guide For The Perplexed
-20% with code BOOKS
In stock at our supplier
TOP
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
-20% with code BOOKS
In stock at our supplier
TOP
HBR Guide to Generative AI for Managers
-20% with code BOOKS
In stock at our supplier
TOP
Speak Data: Artists, Scientists, Thinkers, and Dreamers on How We Live Our Lives in Numbers
-20% with code BOOKS
In stock at our supplier
TOP
If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
Eliezer Yudkowsky, Nate Soares
-20% with code BOOKS
In stock at our supplier
TOP
The Rust Programming Language
Steve Klabnik, Carol Nichols, Chris Krycho
-20% with code BOOKS
In stock at our supplier
Description
The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.
More Information
| Author | Schahin Tofangchi |
|---|---|
| Publisher | Cuvillier |
| Series | Göttinger Wirtschaftsinformatik |
| Release year | 2020 |
| Cover type | Softcover |
| EAN | 9783736972001 |